Reading up on cost-optimization

Several vendors now offer intelligent model routers that automatically select the cheapest sufficient LLM per task, promising 20-30% cost savings, a trend expected to become table stakes.

Routing layer design, not model choice, drives cost efficiency by sending 70-80% of AI traffic to cheap local or async models.

Putting a durable buffer between AI agents and LLM providers prevents token waste and re-billing when a process crashes mid-stream, with resumable streaming and crash recovery using the same mechanism.

Running multiple $10K MRR businesses on a $20/month stack with SQLite, a VPS, and minimal services is possible, but the approach is controversial among developers.